The known global positioning systems (GPS) available in the market are often lack in high precision and usually difficult to directly mark GPS position of the vehicle onto a lane-level map precisely. The GPS technologies experience higher errors in the environment where buildings or tall tree is abundant, such as in urban areas. Even with differential global positioning system (DGPS), wide area augmentation system (WAAS) differential calibration technology to overcome the atmospheric errors, the above techniques are still unable to solve non-line-of-sight (NLOS) problem and multipath errors.
The sources of GPS errors generally include geometric errors, atmospheric ionospheric error, troposphere error, multipath error, and the error of the receiver. The geometric errors, referring to possible errors when using satellite triangulation positioning generated, can be improved by techniques, such as, Positional Dilution of Precision (PDOP), or Horizontal Dilution of Precision (HDOP). The atmospheric ionospheric error and troposphere error usually cause an error in the range of about 3-5 meters, and can be reduced by differential calibration techniques supporting DGPS, WAAS. The multipath error is an error resulted by the satellite signal after multipath, such as, the building of reflection, and the error may be up to 5 meters. The multipath error can be improved through anti-multipath technology. The error of the receiver can be improved through enhanced accuracy and sensitivity of hardware of receiver antenna, clock and other hardware.
In addition, other techniques based on, such as, radar, image recognition, map data, to overcome GPS errors have been made to assist satellite positioning, including positioning supporting Road Side Unit (RSU) lane level (such as, DGPS, WAAS), inertial measurement unit (IMU), a three-dimensional (3D) map data assisted positioning, road scene image database identification positioning, and radar video-assisted positioning and other means. For example, the comparison of the roadside images against an image and map database to determine the position of the vehicle; capturing road-surface data features and the use of the image comparison against an image and map database to determine the position of the vehicle; using detector to detect road features and comparing against database to determine lateral relative position, and so on. Another example of attention in recent years is the Google automatic driverless car. However, all the aforementioned technologies are still limited to the precise positioning of the vehicle itself. Because autopilot vehicle is an upcoming trend with precision positioning as important foundation, therefore, how to achieve the same level of precision positioning for all positioning devices through wireless communication has become an important research topic in the industry.